#encoding:utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# this is data
mnist = input_data.read_data_sets('MNIST_data',one_hot=True)

# hyperparameters
lr = 0.001
training_iters = 100000
batch_size = 128

n_inputs = 28   #MNIST data input (img shape:28x28)
n_steps = 28 # time steps
n_hidden_units = 128 # neurons in hidden layer
n_classes = 10 #MNIST classes (0-9 digits):

# tf Graph input
x = tf.placeholder(tf.float32,[None,n_steps,n_inputs])
y = tf.placeholder(tf.float32,[None,n_classes])

#Define weights
weights = {
    #(28，128）
    'in':tf.Variable(tf.random_normal([n_inputs,n_hidden_units])), 
    #(128,10)
    'out':tf.Variable(tf.random_normal([n_hidden_units,n_classes]))
}
biases = {
    #(128,)
    'in':tf.Variable(tf.constant(0.1,shape=[n_hidden_units,])),
    #(10,)
    'out':tf.Variable(tf.constant(0.1,shape=[n_classes,]))
}

def RNN(X,weighs,biases):
    # hidden layer for input to cell
    ###############################################################
    # X (128 batch, 28 steps, 28 inputs)
    # ==> (128*28 , 28 inputs)
    X = tf.reshape(X,[-1,n_inputs])
    #X_in ==> (128batch*28 steps,128 hidden)
    X_in = tf.matmul(X,weights['in']) + biases['in']
    #X_in ==> (128batch ,28 steps,128 hidden)
    X_in = tf.reshape(X_in,[-1,n_steps,n_hidden_units]) 
    
    #cell
    ###############################################################
    lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden_units,forget_bias=1.0,state_is_tuple=True)
    #lstm cell is divided into two parts (c_state,m_state)
    _init_state = lstm_cell.zero_state(batch_size,dtype=tf.float32)
    
    outputs,states = tf.nn.dynamic_rnn(lstm_cell,X_in,initial_state=_init_state,time_major=False)
    

    #hidden layer for output as the final results
    ############################################################### 
    results = tf.matmul(states[1],weights['out']) + biases['out']
    
    #or
    #unpack to list [(batch, outputs)..]*steps
#     outputs = tf.unstack(tf.transpose(outputs,[1,0,2]))
#     results = tf.matmul(outputs[-1],weights['out'])+biases['out']
    return results
    
    
pred = RNN(x,weights,biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,labels=y))
train_op = tf.train.AdamOptimizer(lr).minimize(cost)

correct_pred = tf.equal(tf.argmax(pred,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))
    
init = tf.initialize_all_variables()
with tf.Session() as sess:
    sess.run(init)
    step = 0
    while step * batch_size < training_iters:
        batch_xs,batch_ys = mnist.train.next_batch(batch_size)
        batch_xs = batch_xs.reshape([batch_size,n_steps,n_inputs])
        sess.run([train_op],feed_dict={
            x:batch_xs,
            y:batch_ys
        })
        if step % 20 == 0:
            print(sess.run(accuracy,feed_dict={
                x:batch_xs,
                y:batch_ys
            }))
        step = step+1
    
    
    
    
    
    
    
    
    
    
    
    
    